Triple
T26508593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patta Mahishi |
E669618
|
entity |
| Predicate | isMaritalRelationOf |
P64467
|
FINISHED |
| Object | king |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: king | Statement: [Patta Mahishi, isMaritalRelationOf, king]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMaritalRelationOf Context triple: [Patta Mahishi, isMaritalRelationOf, king]
-
A.
maritalRelations
chosen
Indicates a legally or socially recognized spousal relationship or marriage-based connection between two entities.
-
B.
relationshipToSpouse
Indicates the specific familial or social role one person holds in relation to their spouse (e.g., husband, wife, partner).
-
C.
spouseOfType
Indicates that one entity is the spouse of another, specifying the type or role of that spousal relationship.
-
D.
hasMaritalRelationshipType
Indicates the specific type or nature of the marital relationship that exists between two entities.
-
E.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69eeb319ec70819090834c2591cf5f1e |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f6138fa6e881908d60d7d354ee2b4e |
completed | May 2, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69f602d5c8808190a1fdbebd6f0981e8 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 27, 2026, 1:18 a.m.